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Fully automated assessment of the knee alignment on long leg radiographs following corrective knee osteotomies in patients with valgus or varus deformities

  • Orthopaedic Surgery
  • Published:
Archives of Orthopaedic and Trauma Surgery Aims and scope Submit manuscript

Abstract

Introduction

The assessment of the knee alignment on long leg radiographs (LLR) postoperative to corrective knee osteotomies (CKOs) is highly dependent on the reader’s expertise. Artificial Intelligence (AI) algorithms may help automate and standardise this process. The study aimed to analyse the reliability of an AI-algorithm for the evaluation of LLRs following CKOs.

Materials and methods

In this study, we analysed a validation cohort of 110 postoperative LLRs from 102 patients. All patients underwent CKO, including distal femoral (DFO), high tibial (HTO) and bilevel osteotomies. The agreement between manual measurements and the AI-algorithm was assessed for the mechanical axis deviation (MAD), hip knee ankle angle (HKA), anatomical-mechanical-axis-angle (AMA), joint line convergence angle (JLCA), mechanical lateral proximal femur angle (mLPFA), mechanical lateral distal femoral angle (mLDFA), mechanical medial proximal tibia angle (mMPTA) and mechanical lateral distal tibia angle (mLDTA), using the intra-class-correlation (ICC) coefficient between the readers, each reader and the AI and the mean of the manual reads and the AI-algorithm and Bland–Altman Plots between the manual reads and the AI software for the MAD, HKA, mLDFA and mMPTA.

Results

In the validation cohort, the AI software showed excellent agreement with the manual reads (ICC: 0.81–0.99). The agreement between the readers (Inter-rater) showed excellent correlations (ICC: 0.95–0. The mean difference in the DFO group for the MAD, HKA, mLDFA and mMPTA were 0.50 mm, − 0.12°, 0.55° and 0.15°. In the HTO group the mean difference for the MAD, HKA, mLDFA and mMPTA were 0.36 mm, − 0.17°, 0.57° and 0.08°, respectively. Reliable outputs were generated in 95.4% of the validation cohort.

Conclusion

 he application of AI-algorithms for the assessment of lower limb alignment on LLRs following CKOs shows reliable and accurate results.

Level of evidence

Diagnostic Level III.

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Data availability

All data supporting the findings of this study are available within the paper. The raw data that of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. Data are located in controlled access data storage at the Orthopaedic Hospital Speising, Vienna, Austria.

References

  1. Fakhrai N, Widhalm P, Chiari C, Weber M, Langs G, Donner R, Ringl H, Jantsch M, Peloschek P (2010) Automatic assessment of the knee alignment angle on full-limb radiographs. Eur J Radiol 74:236–240. https://doi.org/10.1016/j.ejrad.2009.02.004

    Article  PubMed  Google Scholar 

  2. Dawson MJ, Ollivier M, Menetrey J, Beaufils P (2022) Osteotomy around the painful degenerative varus knee: a 2022 ESSKA formal consensus. Knee Surg Sport Traumatol Arthrosc. https://doi.org/10.1007/s00167-022-07024-0

    Article  Google Scholar 

  3. Smith JO, Wilson AJ, Thomas NP (2013) Osteotomy around the knee: evolution, principles and results. Knee Surg Sport Traumatol Arthrosc 21:3–22. https://doi.org/10.1007/s00167-012-2206-0

    Article  CAS  Google Scholar 

  4. Eberbach H, Mehl J, Feucht MJ, Bode G, Südkamp NP, Niemeyer P (2017) Geometry of the valgus knee: contradicting the dogma of a femoral-based deformity. Am J Sports Med 45:909–914. https://doi.org/10.1177/0363546516676266

    Article  PubMed  Google Scholar 

  5. Briem K, Ramsey DK, Newcomb W, Rudolph KS, Snyder-Mackler L (2007) Effects of the amount of valgus correction for medial compartment knee osteoarthritis on clinical outcome, knee kinetics and muscle co-contraction after opening wedge high tibial osteotomy. J Orthop Res 25:311–318. https://doi.org/10.1002/jor.20326

    Article  PubMed  PubMed Central  Google Scholar 

  6. Luís NM, Varatojo R (2021) Radiological assessment of lower limb alignment. EFORT Open Rev 6:487–494. https://doi.org/10.1302/2058-5241.6.210015

    Article  Google Scholar 

  7. Dexel J, Kirschner S, Günther K-P, Lützner J (2014) Agreement between radiological and computer navigation measurement of lower limb alignment. Knee Surg Sport Traumatol Arthrosc 22:2721–2727. https://doi.org/10.1007/s00167-013-2599-4

    Article  Google Scholar 

  8. Prijs J, Liao Z, Ashkani-Esfahani S, Olczak J, Gordon M, Jayakumar P, Jutte PC, Jaarsma RL, IJpma FFA, Doornberg JN (2022) Artificial intelligence and computer vision in orthopaedic trauma. Bone Jt J 104-B:911–914. https://doi.org/10.1302/0301-620X.104B8.BJJ-2022-0119.R1

    Article  Google Scholar 

  9. Ramkumar PN, Kunze KN, Haeberle HS, Karnuta JM, Luu BC, Nwachukwu BU, Williams RJ (2021) Clinical and research medical applications of artificial intelligence. Arthrosc J Arthrosc Relat Surg 37:1694–1697. https://doi.org/10.1016/j.arthro.2020.08.009

    Article  Google Scholar 

  10. Tiulpin A, Thevenot J, Rahtu E, Lehenkari P, Saarakkala S (2018) Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach. Sci Rep 8:1–10. https://doi.org/10.1038/s41598-018-20132-7

    Article  CAS  Google Scholar 

  11. Schock J, Truhn D, Abrar DB, Merhof D, Conrad S, Post M, Mittelstrass F, Kuhl C, Nebelung S (2021) Automated analysis of alignment in long-leg radiographs by using a fully automated support system based on artificial intelligence. Radiol Artif Intell 3:e200198. https://doi.org/10.1148/ryai.2020200198

    Article  PubMed  Google Scholar 

  12. Simon S, Schwarz GM, Aichmair A, Frank BJH, Hummer A, DiFranco MD, Dominkus M, Hofstaetter JG (2022) Fully automated deep learning for knee alignment assessment in lower extremity radiographs: a cross-sectional diagnostic study. Skeletal Radiol 51:1249–1259. https://doi.org/10.1007/s00256-021-03948-9

    Article  PubMed  Google Scholar 

  13. Schwarz GM, Simon S, Mitterer JA, Frank BJH, Aichmair A, Dominkus M, Hofstaetter JG (2022) Artificial intelligence enables reliable and standardized measurements of implant alignment in long leg radiographs with total knee arthroplasties. Knee Surg Sport Traumatol Arthrosc 30:2538–2547. https://doi.org/10.1007/s00167-022-07037-9

    Article  Google Scholar 

  14. Webb JI, Stoner RS, Afzal I, Evans CR, Scott G, Field RE (2021) The Medial Rotation Knee replacement: clinical and radiological results of a multi-centre surveillance study at five years. Knee 28:247–255. https://doi.org/10.1016/j.knee.2020.12.025

    Article  PubMed  Google Scholar 

  15. Paley D (2002) Principles of deformity correction. Springer Berlin Heidelberg, Berlin, Heidelberg

    Book  Google Scholar 

  16. Koo TK, Li MY (2016) A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 15:155–163. https://doi.org/10.1016/j.jcm.2016.02.012

    Article  PubMed  PubMed Central  Google Scholar 

  17. Kowe M (2019) Die intra-cluster-correlation im kontext der fallzahlschätzung cluster-randomisierter studien in der pegewissenschaft. Philosophisch-Theologischen Hochschule Vallendar

  18. Hirschmann MT, Moser LB, Amsler F, Behrend H, Leclerq V, Hess S (2019) Functional knee phenotypes: a novel classification for phenotyping the coronal lower limb alignment based on the native alignment in young non-osteoarthritic patients. Knee Surg Sport Traumatol Arthrosc 27:1394–1402. https://doi.org/10.1007/s00167-019-05509-z

    Article  Google Scholar 

  19. Knutson GA (2005) Anatomic and functional leg-length inequality: a review and recommendation for clinical decision-making. Part I, anatomic leg-length inequality: prevalence, magnitude, effects and clinical significance. Chiropr Osteopat 13:1–10. https://doi.org/10.1186/1746-1340-13-11

    Article  Google Scholar 

  20. Obuchowski NA, Subhas N, Schoenhagen P (2014) Testing for interchangeability of imaging tests. Acad Radiol 21:1483–1489. https://doi.org/10.1016/j.acra.2014.07.004

    Article  PubMed  Google Scholar 

  21. Matsuura M, Murata H, Fujino Y, Hirasawa K, Yanagisawa M, Asaoka R (2018) Evaluating the usefulness of MP-3 microperimetry in glaucoma patients. Am J Ophthalmol 187:1–9. https://doi.org/10.1016/j.ajo.2017.12.002

    Article  PubMed  Google Scholar 

  22. Bernstein P, Metzler J, Weinzierl M, Seifert C, Kisel W, Wacker M (2021) Radiographic scoliosis angle estimation: spline-based measurement reveals superior reliability compared to traditional COBB method. Eur Spine J 30:676–685. https://doi.org/10.1007/s00586-020-06577-3

    Article  PubMed  Google Scholar 

  23. Jamali AA, Meehan JP, Moroski NM, Anderson MJ, Lamba R, Parise C (2017) Do small changes in rotation affect measurements of lower extremity limb alignment? J Orthop Surg Res 12:1–8. https://doi.org/10.1186/s13018-017-0571-6

    Article  Google Scholar 

  24. Ahmad SS, Weinrich L, Giebel GM, Beyer MR, Stöckle U, Konrads C (2021) Frontal knee alignment influences the vertical orientation of the femoral neck in standing position. Bone Jt Open 2:1057–1061. https://doi.org/10.1302/2633-1462.212.BJO-2021-0100.R1

    Article  PubMed  PubMed Central  Google Scholar 

  25. Kunze KN, Polce EM, Patel A, Courtney PM, Levine BR (2021) Validation and performance of a machine-learning derived prediction guide for total knee arthroplasty component sizing. Arch Orthop Trauma Surg 141:2235–2244. https://doi.org/10.1007/s00402-021-04041-5

    Article  PubMed  Google Scholar 

  26. Tsai A (2022) A deep learning approach to automatically quantify lower extremity alignment in children. Skelet Radiol 51:381–390. https://doi.org/10.1007/s00256-021-03844-2

    Article  Google Scholar 

  27. Hirschmann A, Cyriac J, Stieltjes B, Kober T, Richiardi J, Omoumi P (2019) Artificial intelligence in musculoskeletal imaging: review of current literature, challenges, and trends. Semin Musculoskelet Radiol 23:304–311. https://doi.org/10.1055/s-0039-1684024

    Article  PubMed  Google Scholar 

  28. Schwarz GM, Simon S, Mitterer JA, Huber S, Frank BJH, Aichmair A, Dominkus M, Hofstaetter JG (2023) Can an artificial intelligence powered software reliably assess pelvic radiographs. Int Orthop. https://doi.org/10.1007/s00264-023-05722-z

    Article  PubMed  Google Scholar 

  29. Langerhuizen DWG, Janssen SJ, Mallee WH, Van Den Bekerom MPJ, Ring D, Kerkhoffs GMMJ, Jaarsma RL, Doornberg JN (2019) What are the applications and limitations of artificial intelligence for fracture detection and classification in orthopaedic trauma imaging? A systematic review. Clin Orthop Relat Res 477:2482–2491. https://doi.org/10.1097/CORR.0000000000000848

    Article  PubMed  PubMed Central  Google Scholar 

  30. Sailhan F, Jacob L, Hamadouche M (2017) Differences in limb alignment and femoral mechanical-anatomical angles using two dimension versus three dimension radiographic imaging. Int Orthop 41:2009–2016. https://doi.org/10.1007/s00264-017-3428-z

    Article  PubMed  Google Scholar 

  31. Lee NK, Kim TW, Lee S, Choi YS, Kang SB, Chang CB (2021) Effect of distance between the feet on knee joint line orientation after total knee arthroplasty in standing full-limb radiographs. Knee Surg Sport Traumatol Arthrosc 30:3032–3040. https://doi.org/10.1007/s00167-021-06662-0

    Article  Google Scholar 

  32. Klemt C, Cohen-Levy WB, Robinson MG, Burns JC, Alpaugh K, Yeo I, Kwon Y-M (2022) Can machine learning models predict failure of revision total hip arthroplasty? Arch Orthop Trauma Surg 143:2805–2812. https://doi.org/10.1007/s00402-022-04453-x

    Article  PubMed  Google Scholar 

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Acknowledgements

We would like to acknowledge Matthew D. DiFranco, PhD and Allan Hummer, PhD for the technical support, Bernd Otzelberger for statistical support and Susana Gardete-Hartmann, PhD for conceptualization.

Funding

The “Michael Ogon Laboratory for Orthopaedic Research” received a research grant from “Image Biopsy Lab GmbH”. The collection, analysis, and interpretation of data, writing of the report, and the decision to submit the paper for publication were performed by the authors and not influenced by Image Biopsy Lab.

Author information

Authors and Affiliations

  1. Michael Ogon Laboratory for Orthopaedic Research, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria

    Jennyfer A. Mitterer, Stephanie Huber, Gilbert M. Schwarz, Sebastian Simon, Bernhard J. H. Frank & Jochen G. Hofstaetter

  2. Center for Anatomy and Cell Biology, Medical University Vienna Speising, Währinger Straße 13, 1090, Vienna, Austria

    Stephanie Huber & Gilbert M. Schwarz

  3. Department of Orthopaedic and Trauma-Surgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria

    Gilbert M. Schwarz

  4. 2nd Department, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria

    Sebastian Simon, Bernhard J. H. Frank & Jochen G. Hofstaetter

  5. Department of Pediatric Orthopaedics, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria

    Matthias Pallamar

  6. 1st Department, Orthopaedic Hospital Vienna Speising, Speisinger Straße 109, 1130, Vienna, Austria

    Florian Kissler

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Contributions

All authors contributed to the study conception and design. JAM, SH, SS, MP, FK and BJHF performed material preparation, data collection and analysis. JAM wrote the first draft of the manuscript and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Conceptualization and Methodology: JAM, SH, GMS, SS and JGH. Formal analysis and investigation: JAM, SH, GMS and JGH. Writing—original draft preparation: JAM. Writing—review and editing: JAM, SH, GMS and JGH. Funding acquisition, Resources and Supervision: JGH.

Corresponding author

Correspondence to Jochen G. Hofstaetter.

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Conflict of Interest

There is no agreement with commercial interest related to this study, which would limit or delay the publication in any way or would limit all data generated for the study.

Ethical approval

EK 46/2020

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Individual informed consent was waived.

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Mitterer, J.A., Huber, S., Schwarz, G.M. et al. Fully automated assessment of the knee alignment on long leg radiographs following corrective knee osteotomies in patients with valgus or varus deformities. Arch Orthop Trauma Surg 144, 1029–1038 (2024). https://doi.org/10.1007/s00402-023-05151-y

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